# !pip install git+https://github.com/alberanid/imdbpy
# !pip install pandas
# !pip install numpy
# !pip install matplotlib
# !pip install seaborn
# !pip install pandas_profiling --upgrade
# !pip install plotly
# !pip install wordcloud
# !pip install Flask

# Import Dataset
# Import File from Loacal Drive
# from google.colab import files
# data_to_load = files.upload()
# from google.colab import drive
# drive.mount('/content/drive')

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import collections
import plotly.express as px
import plotly.graph_objects as go
import nltk
import re
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist
from nltk.util import ngrams
from plotly.subplots import make_subplots
from plotly.offline import iplot, init_notebook_mode
from wordcloud import WordCloud, STOPWORDS
from pandas_profiling import ProfileReport
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
%matplotlib inline
warnings.filterwarnings("ignore")

nltk.download('all')

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[nltk_data]    |   Package vader_lexicon is already up-to-date!
[nltk_data]    | Downloading package porter_test to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package porter_test is already up-to-date!
[nltk_data]    | Downloading package wmt15_eval to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package wmt15_eval is already up-to-date!
[nltk_data]    | Downloading package mwa_ppdb to
[nltk_data]    |     C:\Users\pawan\AppData\Roaming\nltk_data...
[nltk_data]    |   Package mwa_ppdb is already up-to-date!
[nltk_data]    | 
[nltk_data]  Done downloading collection all

True

# path = '/content/drive/MyDrive/Files/'

path = 'C:\\Users\\pawan\\OneDrive\\Desktop\\ott\\Data\\'
 
df_tvshows = pd.read_csv(path + 'otttvshows.csv')
 
df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	Language	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type
0	1	Snowpiercer	2013	18+	6.9	94%	NaN	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	English	Set seven years after the world has become a f...	60.0	tv series	3.0	1	0	0	0	1
1	2	Philadelphia	1993	13+	8.8	80%	NaN	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	English	The gang, 5 raging alcoholic, narcissists run ...	22.0	tv series	18.0	1	0	0	0	1
2	3	Roma	2018	18+	8.7	93%	NaN	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	English	In this British historical drama, the turbulen...	52.0	tv series	2.0	1	0	0	0	1
3	4	Amy	2015	18+	7.0	87%	NaN	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	English	A family drama focused on three generations of...	60.0	tv series	6.0	1	0	1	1	1
4	5	The Young Offenders	2016	NaN	8.0	100%	NaN	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	English	NaN	30.0	tv series	3.0	1	0	0	0	1

# profile = ProfileReport(df_tvshows)
# profile

def data_investigate(df):
    print('No of Rows : ', df.shape[0])
    print('No of Coloums : ', df.shape[1])
    print('**'*25)
    print('Colums Names : \n', df.columns)
    print('**'*25)
    print('Datatype of Columns : \n', df.dtypes)
    print('**'*25)
    print('Missing Values : ')
    c = df.isnull().sum()
    c = c[c > 0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise :\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation : ')
    plt.figure(figsize = (10, 10))
    sns.heatmap(df.isnull(), yticklabels = False, cbar = False)
    plt.show()

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  20
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime            float64
Kind                object
Seasons            float64
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
dtype: object
**************************************************
Missing Values : 
Age                1954
IMDb                556
Rotten Tomatoes    4194
Directors          5158
Cast                486
Genres              323
Country             549
Language            638
Plotline           2493
Runtime            1410
Seasons             679
dtype: int64
**************************************************
Missing vaules %age wise :

ID                  0.000000
Title               0.000000
Year                0.000000
Age                35.972018
IMDb               10.235641
Rotten Tomatoes    77.209131
Directors          94.955817
Cast                8.946981
Genres              5.946244
Country            10.106775
Language           11.745214
Plotline           45.894698
Runtime            25.957290
Kind                0.000000
Seasons            12.500000
Netflix             0.000000
Hulu                0.000000
Prime Video         0.000000
Disney+             0.000000
Type                0.000000
dtype: float64
**************************************************
Pictorial Representation : 

# ID
# df_tvshows = df_tvshows.drop(['ID'], axis = 1)
 
# Age
df_tvshows.loc[df_tvshows['Age'].isnull() & df_tvshows['Disney+'] == 1, "Age"] = '13'
# df_tvshows.fillna({'Age' : 18}, inplace = True)
df_tvshows.fillna({'Age' : 'NR'}, inplace = True)
df_tvshows['Age'].replace({'all': '0'}, inplace = True)
df_tvshows['Age'].replace({'7+': '7'}, inplace = True)
df_tvshows['Age'].replace({'13+': '13'}, inplace = True)
df_tvshows['Age'].replace({'16+': '16'}, inplace = True)
df_tvshows['Age'].replace({'18+': '18'}, inplace = True)
# df_tvshows['Age'] = df_tvshows['Age'].astype(int)
 
# IMDb
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].mean()}, inplace = True)
# df_tvshows.fillna({'IMDb' : df_tvshows['IMDb'].median()}, inplace = True)
df_tvshows.fillna({'IMDb' : "NA"}, inplace = True)
 
# Rotten Tomatoes
df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].str.replace('%', '').astype(int)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'][df_tvshows['Rotten Tomatoes'].notnull()].astype(int)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].mean()}, inplace = True)
# df_tvshows.fillna({'Rotten Tomatoes' : df_tvshows['Rotten Tomatoes'].median()}, inplace = True)
# df_tvshows['Rotten Tomatoes'] = df_tvshows['Rotten Tomatoes'].astype(int)
df_tvshows.fillna({'Rotten Tomatoes' : "NA"}, inplace = True)
 
# Directors
# df_tvshows = df_tvshows.drop(['Directors'], axis = 1)
df_tvshows.fillna({'Directors' : "NA"}, inplace = True)
 
# Cast
df_tvshows.fillna({'Cast' : "NA"}, inplace = True)
 
# Genres
df_tvshows.fillna({'Genres': "NA"}, inplace = True)
 
# Country
df_tvshows.fillna({'Country': "NA"}, inplace = True)
 
# Language
df_tvshows.fillna({'Language': "NA"}, inplace = True)
 
# Plotline
df_tvshows.fillna({'Plotline': "NA"}, inplace = True)
 
# Runtime
# df_tvshows.fillna({'Runtime' : df_tvshows['Runtime'].mean()}, inplace = True)
# df_tvshows['Runtime'] = df_tvshows['Runtime'].astype(int)
df_tvshows.fillna({'Runtime' : "NA"}, inplace = True)
 
# Kind
# df_tvshows.fillna({'Kind': "NA"}, inplace = True)
 
# Type
# df_tvshows.fillna({'Type': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Type'], axis = 1)
 
# Seasons
# df_tvshows.fillna({'Seasons': 1}, inplace = True)
df_tvshows.fillna({'Seasons': "NA"}, inplace = True)
# df_tvshows = df_tvshows.drop(['Seasons'], axis = 1)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
# df_tvshows.fillna({'Seasons' : df_tvshows['Seasons'].mean()}, inplace = True)
# df_tvshows['Seasons'] = df_tvshows['Seasons'].astype(int)
 
# Service Provider
df_tvshows['Service Provider'] = df_tvshows.loc[:, ['Netflix', 'Prime Video', 'Disney+', 'Hulu']].idxmax(axis = 1)
# df_tvshows.drop(['Netflix','Prime Video','Disney+','Hulu'], axis = 1)

# Removing Duplicate and Missing Entries
df_tvshows.dropna(how = 'any', inplace = True)
df_tvshows.drop_duplicates(inplace = True)

data_investigate(df_tvshows)

No of Rows :  5432
No of Coloums :  21
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
dtype: float64
**************************************************
Pictorial Representation : 

df_tvshows.head()

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Plotline	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	...	Set seven years after the world has become a f...	60	tv series	3	1	0	0	0	1	Netflix
1	2	Philadelphia	1993	13	8.8	80	NA	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	Comedy	United States	...	The gang, 5 raging alcoholic, narcissists run ...	22	tv series	18	1	0	0	0	1	Netflix
2	3	Roma	2018	18	8.7	93	NA	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	...	In this British historical drama, the turbulen...	52	tv series	2	1	0	0	0	1	Netflix
3	4	Amy	2015	18	7	87	NA	Amy Brenneman,Richard T. Jones,Jessica Tuck,Ma...	Drama	United States	...	A family drama focused on three generations of...	60	tv series	6	1	0	1	1	1	Netflix
4	5	The Young Offenders	2016	NR	8	100	NA	Alex Murphy,Chris Walley,Hilary Rose,Dominic M...	Comedy	United Kingdom,Ireland	...	NA	30	tv series	3	1	0	0	0	1	Netflix

5 rows × 21 columns

df_tvshows.describe()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
count	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.000000	5432.0
mean	2716.500000	2010.668446	0.341311	0.293999	0.403351	0.033689	1.0
std	1568.227662	11.726176	0.474193	0.455633	0.490615	0.180445	0.0
min	1.000000	1901.000000	0.000000	0.000000	0.000000	0.000000	1.0
25%	1358.750000	2009.000000	0.000000	0.000000	0.000000	0.000000	1.0
50%	2716.500000	2014.000000	0.000000	0.000000	0.000000	0.000000	1.0
75%	4074.250000	2017.000000	1.000000	1.000000	1.000000	0.000000	1.0
max	5432.000000	2020.000000	1.000000	1.000000	1.000000	1.000000	1.0

df_tvshows.corr()

	ID	Year	Netflix	Hulu	Prime Video	Disney+	Type
ID	1.000000	-0.031346	-0.646330	0.034293	0.441264	0.195409	NaN
Year	-0.031346	1.000000	0.222316	-0.065807	-0.198675	-0.022741	NaN
Netflix	-0.646330	0.222316	1.000000	-0.366515	-0.515086	-0.119344	NaN
Hulu	0.034293	-0.065807	-0.366515	1.000000	-0.377374	-0.075701	NaN
Prime Video	0.441264	-0.198675	-0.515086	-0.377374	1.000000	-0.151442	NaN
Disney+	0.195409	-0.022741	-0.119344	-0.075701	-0.151442	1.000000	NaN
Type	NaN	NaN	NaN	NaN	NaN	NaN	NaN

# df_tvshows.sort_values('Year', ascending = True)
# df_tvshows.sort_values('IMDb', ascending = False)

# df_tvshows.to_csv(path_or_buf= '/content/drive/MyDrive/Files/updated_otttvshows.csv', index = False)
 
# path = '/content/drive/MyDrive/Files/'
 
# udf_tvshows = pd.read_csv(path + 'updated_otttvshows.csv')
 
# udf_tvshows

# df_netflix_tvshows = df_tvshows.loc[(df_tvshows['Netflix'] > 0)]
# df_hulu_tvshows = df_tvshows.loc[(df_tvshows['Hulu'] > 0)]
# df_prime_video_tvshows = df_tvshows.loc[(df_tvshows['Prime Video'] > 0)]
# df_disney_tvshows = df_tvshows.loc[(df_tvshows['Disney+'] > 0)]

df_netflix_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 1) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_hulu_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 1) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 0)]
df_prime_video_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 1 ) & (df_tvshows['Disney+'] == 0)]
df_disney_only_tvshows = df_tvshows[(df_tvshows['Netflix'] == 0) & (df_tvshows['Hulu'] == 0) & (df_tvshows['Prime Video'] == 0 ) & (df_tvshows['Disney+'] == 1)]

df_tvshows_genres = df_tvshows.copy()

df_tvshows_genres.drop(df_tvshows_genres.loc[df_tvshows_genres['Genres'] == "NA"].index, inplace = True)
# df_tvshows_genres = df_tvshows_genres[df_tvshows_genres.Genre != "NA"]
# df_tvshows_genres['Genres'] = df_tvshows_genres['Genres'].astype(str)

df_tvshows_count_genres = df_tvshows_genres.copy()

df_tvshows_genre = df_tvshows_genres.copy()

df_tvshows_genre_all = df_tvshows_genres.copy()

# Create genres dict where key=name and value = number of genres
 
genres = {}
 
for i in df_tvshows_count_genres['Genres'].dropna():
    if i != "NA":
        #print(i,len(i.split(',')))
        genres[i] = len(i.split(','))
    else:
        genres[i] = 0
    
# Add this information to our dataframe as a new column
 
df_tvshows_count_genres['Number of Genres'] = df_tvshows_count_genres['Genres'].map(genres).astype(int)

df_tvshows_mixed_genres = df_tvshows_count_genres.copy()

# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_genres_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Netflix'] == 1]
hulu_genres_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Hulu'] == 1]
prime_video_genres_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Prime Video'] == 1]
disney_genres_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Disney+'] == 1]

plt.figure(figsize = (10, 10))
corr = df_tvshows_count_genres.corr()
# Plot figsize
fig, ax = plt.subplots(figsize=(10, 8))
# Generate Heat Map, alleast annotations and place floats in map
sns.heatmap(corr, cmap = 'magma', annot = True, fmt = ".2f")
# Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
# Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
# show plot
plt.show()
fig.show()

<Figure size 720x720 with 0 Axes>

df_genres_most_tvshows = df_tvshows_count_genres.sort_values(by = 'Number of Genres', ascending = False).reset_index()
df_genres_most_tvshows = df_genres_most_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_count_genres['Number of Genres'] == (df_tvshows_count_genres['Number of Genres'].max()))
# df_genres_most_tvshows = df_tvshows_count_genres[filter]
 
# mostest_rated_tvshows = df_tvshows_count_genres.loc[df_tvshows_count_genres['Number of Genres'].idxmax()]
 
print('\nTV Shows with Highest Ever Number of Genres are : \n')
df_genres_most_tvshows.head(5)

TV Shows with Highest Ever Number of Genres are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider	Number of Genres
0	2406	Steven Universe	2013	7	8.2	100	NA	Zach Callison,Deedee Magno,Michaela Dietz,Este...	Animation,Action,Adventure,Comedy,Drama,Family...	United States,South Korea,Spain,Japan,Mexico	...	11	tv series	6	0	1	0	0	1	Hulu	11
1	5314	Gargoyles	1994	7	8.1	NA	NA	Keith David,Salli Richardson-Whitfield,Jeff Be...	Animation,Action,Adventure,Crime,Drama,Family,...	United States	...	30	tv series	2	0	0	0	1	1	Disney+	10
2	2275	Gravity Falls	2012	7	8.9	100	NA	Jason Ritter,Alex Hirsch,Kristen Schaal,Linda ...	Animation,Adventure,Comedy,Drama,Family,Fantas...	United States,Argentina,Australia,United Kingd...	...	23	tv series	2	0	1	0	1	1	Disney+	10
3	3896	Infinity Train	2019	7	8.5	100	NA	Ashley Johnson,Owen Dennis,Jeremy Crutchley,Ki...	Animation,Action,Adventure,Drama,Family,Fantas...	United States	...	11	tv series	4	0	0	1	0	1	Prime Video	10
4	2096	Spy Kids: Mission Critical	2018	7	4.7	NA	NA	Nicholas Coombe,Ashley Bornancin,Carter Hastin...	Animation,Action,Adventure,Comedy,Drama,Family...	United States,Canada	...	NA	tv series	3	1	0	0	0	1	Netflix	9

5 rows × 22 columns

fig = px.bar(y = df_genres_most_tvshows['Title'][:15],
             x = df_genres_most_tvshows['Number of Genres'][:15], 
             color = df_genres_most_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Highest Number of Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

df_genres_least_tvshows = df_tvshows_count_genres.sort_values(by = 'Number of Genres', ascending = True).reset_index()
df_genres_least_tvshows = df_genres_least_tvshows.drop(['index'], axis = 1)
# filter = (df_tvshows_count_genres['Number of Genres'] == (df_tvshows_count_genres['Number of Genres'].min()))
# df_genres_least_tvshows = df_tvshows_count_genres[filter]

print('\nTV Shows with Lowest Ever Number of Genres are : \n')
df_genres_least_tvshows.head(5)

TV Shows with Lowest Ever Number of Genres are : 

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider	Number of Genres
0	5431	Love & Vets	2017	13	8.6	NA	NA	Will Draper,Francoise Tyler,Olivia,Carrie McCo...	Reality-TV	United States	...	42	tv series	1	0	0	0	1	1	Disney+	1
1	1528	Louis Theroux: Miami Mega-Jail	2011	18	7.6	NA	NA	Louis Theroux	Documentary	United Kingdom	...	120	tv series	1	1	0	0	0	1	Netflix	1
2	3466	Tengo Talento, Mucho Talento	2012	NR	7.2	NA	NA	Mary Fredette,Denisse Padilla,Elena Diaz,Cesar...	Reality-TV	United States	...	NA	tv series	18	0	1	0	0	1	Hulu	1
3	3468	Vets Saving Pets	2018	NR	7.4	NA	NA	Ed Nash	Documentary	United States	...	NA	tv series	2	0	1	0	0	1	Hulu	1
4	3469	The View	1997	16	2.6	NA	NA	Whoopi Goldberg,Joy Behar,Virginia Hamilton,Su...	Talk-Show	United States	...	60	tv series	NA	0	1	0	0	1	Hulu	1

5 rows × 22 columns

fig = px.bar(y = df_genres_least_tvshows['Title'][:15],
             x = df_genres_least_tvshows['Number of Genres'][:15], 
             color = df_genres_least_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Lowest Number of Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      Total '{df_tvshows_count_genres['Number of Genres'].unique().shape[0]}' unique Number of Genres s were Given, They were Like this,\n
      
      {df_tvshows_count_genres.sort_values(by = 'Number of Genres', ascending = False)['Number of Genres'].unique()}\n
 
      The Highest Number of Genres Ever Any TV Show Got is '{df_genres_most_tvshows['Title'][0]}' : '{df_genres_most_tvshows['Number of Genres'].max()}'\n
 
      The Lowest Number of Genres Ever Any TV Show Got is '{df_genres_least_tvshows['Title'][0]}' : '{df_genres_least_tvshows['Number of Genres'].min()}'\n
      ''')

      Total '11' unique Number of Genres s were Given, They were Like this,

      
      [11 10  9  8  7  6  5  4  3  2  1]

 
      The Highest Number of Genres Ever Any TV Show Got is 'Steven Universe' : '11'

 
      The Lowest Number of Genres Ever Any TV Show Got is 'Love & Vets' : '1'

      

netflix_genres_most_tvshows = df_genres_most_tvshows.loc[df_genres_most_tvshows['Netflix']==1].reset_index()
netflix_genres_most_tvshows = netflix_genres_most_tvshows.drop(['index'], axis = 1)
 
netflix_genres_least_tvshows = df_genres_least_tvshows.loc[df_genres_least_tvshows['Netflix']==1].reset_index()
netflix_genres_least_tvshows = netflix_genres_least_tvshows.drop(['index'], axis = 1)
 
netflix_genres_most_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider	Number of Genres
0	2096	Spy Kids: Mission Critical	2018	7	4.7	NA	NA	Nicholas Coombe,Ashley Bornancin,Carter Hastin...	Animation,Action,Adventure,Comedy,Drama,Family...	United States,Canada	...	NA	tv series	3	1	0	0	0	1	Netflix	9
1	723	Case Closed	1996	7	8.4	NA	NA	Minami Takayama,Wakana Yamazaki,Hoang Khuyet,A...	Animation,Action,Adventure,Comedy,Crime,Drama,...	Japan,Italy,United Kingdom,Mexico,Spain	...	25	tv series	53	1	1	0	0	1	Netflix	9
2	810	Daybreak	2019	18	6.7	70	NA	Colin Ford,Alyvia Alyn Lind,Sophie Simnett,Aus...	Action,Adventure,Comedy,Drama,Fantasy,Horror,M...	United States	...	60	tv series	1	1	0	0	0	1	Netflix	9
3	833	Scooby-Doo! Mystery Incorporated	2010	7	8	NA	NA	Frank Welker,Mindy Cohn,Grey Griffin,Matthew L...	Animation,Adventure,Comedy,Crime,Drama,Family,...	United States	...	23	tv series	2	1	0	0	0	1	Netflix	9
4	788	Sword Art Online	2012	18	7.6	NA	NA	Yoshitsugu Matsuoka,Haruka Tomatsu,Bryce Papen...	Animation,Action,Adventure,Comedy,Drama,Fantas...	Japan	...	24	tv series	4	1	1	0	0	1	Netflix	9

5 rows × 22 columns

fig = px.bar(y = netflix_genres_most_tvshows['Title'][:15],
             x = netflix_genres_most_tvshows['Number of Genres'][:15], 
             color = netflix_genres_most_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Highest Number of Genres : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = netflix_genres_least_tvshows['Title'][:15],
             x = netflix_genres_least_tvshows['Number of Genres'][:15], 
             color = netflix_genres_least_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Lowest Number of Genres : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

hulu_genres_most_tvshows = df_genres_most_tvshows.loc[df_genres_most_tvshows['Hulu']==1].reset_index()
hulu_genres_most_tvshows = hulu_genres_most_tvshows.drop(['index'], axis = 1)
 
hulu_genres_least_tvshows = df_genres_least_tvshows.loc[df_genres_least_tvshows['Hulu']==1].reset_index()
hulu_genres_least_tvshows = hulu_genres_least_tvshows.drop(['index'], axis = 1)
 
hulu_genres_most_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider	Number of Genres
0	2406	Steven Universe	2013	7	8.2	100	NA	Zach Callison,Deedee Magno,Michaela Dietz,Este...	Animation,Action,Adventure,Comedy,Drama,Family...	United States,South Korea,Spain,Japan,Mexico	...	11	tv series	6	0	1	0	0	1	Hulu	11
1	2275	Gravity Falls	2012	7	8.9	100	NA	Jason Ritter,Alex Hirsch,Kristen Schaal,Linda ...	Animation,Adventure,Comedy,Drama,Family,Fantas...	United States,Argentina,Australia,United Kingd...	...	23	tv series	2	0	1	0	1	1	Disney+	10
2	2490	Star vs. the Forces of Evil	2015	7	8	NA	NA	Eden Sher,Adam McArthur,Grey Griffin,Daron Nef...	Animation,Action,Adventure,Comedy,Drama,Family...	United States,Spain,United Kingdom,Mexico,Japan	...	22	tv series	4	0	1	0	1	1	Disney+	9
3	2867	OK K.O.! Let's Be Heroes	2017	7	7.1	NA	NA	Courtenay Taylor,Ashly Burch,Ian Jones-Quartey...	Animation,Short,Action,Adventure,Comedy,Family...	United States,South Korea	...	11	tv series	3	0	1	0	0	1	Hulu	9
4	723	Case Closed	1996	7	8.4	NA	NA	Minami Takayama,Wakana Yamazaki,Hoang Khuyet,A...	Animation,Action,Adventure,Comedy,Crime,Drama,...	Japan,Italy,United Kingdom,Mexico,Spain	...	25	tv series	53	1	1	0	0	1	Netflix	9

5 rows × 22 columns

fig = px.bar(y = hulu_genres_most_tvshows['Title'][:15],
             x = hulu_genres_most_tvshows['Number of Genres'][:15], 
             color = hulu_genres_most_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Highest Number of Genres : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = hulu_genres_least_tvshows['Title'][:15],
             x = hulu_genres_least_tvshows['Number of Genres'][:15], 
             color = hulu_genres_least_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Lowest Number of Genres : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

prime_video_genres_most_tvshows = df_genres_most_tvshows.loc[df_genres_most_tvshows['Prime Video']==1].reset_index()
prime_video_genres_most_tvshows = prime_video_genres_most_tvshows.drop(['index'], axis = 1)
 
prime_video_genres_least_tvshows = df_genres_least_tvshows.loc[df_genres_least_tvshows['Prime Video']==1].reset_index()
prime_video_genres_least_tvshows = prime_video_genres_least_tvshows.drop(['index'], axis = 1)
 
prime_video_genres_most_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider	Number of Genres
0	3896	Infinity Train	2019	7	8.5	100	NA	Ashley Johnson,Owen Dennis,Jeremy Crutchley,Ki...	Animation,Action,Adventure,Drama,Family,Fantas...	United States	...	11	tv series	4	0	0	1	0	1	Prime Video	10
1	3806	Eerie, Indiana	1991	7	8.2	100	NA	Omri Katz,Justin Shenkarow,Mary-Margaret Humes...	Adventure,Comedy,Drama,Family,Fantasy,Horror,M...	United States	...	30	tv series	1	0	0	1	0	1	Prime Video	9
2	4027	Bakugan Battle Brawlers	2007	7	5.5	NA	NA	Jason Deline,Julie Lemieux,Carter Hayden,Shawn...	Animation,Action,Adventure,Comedy,Drama,Family...	Canada,South Korea,Japan	...	22	tv series	2	0	0	1	0	1	Prime Video	9
3	4290	GetBackers	2002	7	7.4	NA	NA	Darren Pleavin,Shanon Weaver,Jason Liebrecht,O...	Animation,Action,Adventure,Comedy,Crime,Drama,...	Japan,Italy,United Kingdom,Mexico,United State...	...	24	tv series	1	0	0	1	0	1	Prime Video	9
4	5081	The Fish Guyz	2017	NR	7.3	NA	NA	Rob Paulsen,Jeff Bennett,Cam Clarke,Jim Cummin...	Animation,Action,Adventure,Comedy,Crime,Fantas...	NA	...	NA	tv series	NA	0	0	1	0	1	Prime Video	8

5 rows × 22 columns

fig = px.bar(y = prime_video_genres_most_tvshows['Title'][:15],
             x = prime_video_genres_most_tvshows['Number of Genres'][:15], 
             color = prime_video_genres_most_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Highest Number of Genres : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = prime_video_genres_least_tvshows['Title'][:15],
             x = prime_video_genres_least_tvshows['Number of Genres'][:15], 
             color = prime_video_genres_least_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Lowest Number of Genres : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

disney_genres_most_tvshows = df_genres_most_tvshows.loc[df_genres_most_tvshows['Disney+']==1].reset_index()
disney_genres_most_tvshows = disney_genres_most_tvshows.drop(['index'], axis = 1)
 
disney_genres_least_tvshows = df_genres_least_tvshows.loc[df_genres_least_tvshows['Disney+']==1].reset_index()
disney_genres_least_tvshows = disney_genres_least_tvshows.drop(['index'], axis = 1)
 
disney_genres_most_tvshows.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider	Number of Genres
0	5314	Gargoyles	1994	7	8.1	NA	NA	Keith David,Salli Richardson-Whitfield,Jeff Be...	Animation,Action,Adventure,Crime,Drama,Family,...	United States	...	30	tv series	2	0	0	0	1	1	Disney+	10
1	2275	Gravity Falls	2012	7	8.9	100	NA	Jason Ritter,Alex Hirsch,Kristen Schaal,Linda ...	Animation,Adventure,Comedy,Drama,Family,Fantas...	United States,Argentina,Australia,United Kingd...	...	23	tv series	2	0	1	0	1	1	Disney+	10
2	2490	Star vs. the Forces of Evil	2015	7	8	NA	NA	Eden Sher,Adam McArthur,Grey Griffin,Daron Nef...	Animation,Action,Adventure,Comedy,Drama,Family...	United States,Spain,United Kingdom,Mexico,Japan	...	22	tv series	4	0	1	0	1	1	Disney+	9
3	5321	TaleSpin	1990	0	7.6	NA	NA	Ed Gilbert,Jim Cummings,Sally Struthers,R.J. W...	Animation,Action,Adventure,Comedy,Drama,Family...	United States	...	30	tv series	1	0	0	0	1	1	Disney+	8
4	5373	Randy Cunningham: 9th Grade Ninja	2012	7	6.7	NA	NA	Ben Schwartz,Andrew Lewis Caldwell,Facundo Rey...	Animation,Short,Action,Adventure,Comedy,Family...	United Kingdom,United States,Ireland	...	15	tv series	2	0	0	0	1	1	Disney+	8

5 rows × 22 columns

fig = px.bar(y = disney_genres_most_tvshows['Title'][:15],
             x = disney_genres_most_tvshows['Number of Genres'][:15], 
             color = disney_genres_most_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Highest Number of Genres : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

fig = px.bar(y = disney_genres_least_tvshows['Title'][:15],
             x = disney_genres_least_tvshows['Number of Genres'][:15], 
             color = disney_genres_least_tvshows['Number of Genres'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Genres'},
             title  = 'TV Shows with Lowest Number of Genres : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      The TV Show with Highest Number of Genres Ever Got is '{df_genres_most_tvshows['Title'][0]}' : '{df_genres_most_tvshows['Number of Genres'].max()}'\n
      The TV Show with Lowest Number of Genres Ever Got is '{df_genres_least_tvshows['Title'][0]}' : '{df_genres_least_tvshows['Number of Genres'].min()}'\n
      
      The TV Show with Highest Number of Genres on 'Netflix' is '{netflix_genres_most_tvshows['Title'][0]}' : '{netflix_genres_most_tvshows['Number of Genres'].max()}'\n
      The TV Show with Lowest Number of Genres on 'Netflix' is '{netflix_genres_least_tvshows['Title'][0]}' : '{netflix_genres_least_tvshows['Number of Genres'].min()}'\n
      
      The TV Show with Highest Number of Genres on 'Hulu' is '{hulu_genres_most_tvshows['Title'][0]}' : '{hulu_genres_most_tvshows['Number of Genres'].max()}'\n
      The TV Show with Lowest Number of Genres on 'Hulu' is '{hulu_genres_least_tvshows['Title'][0]}' : '{hulu_genres_least_tvshows['Number of Genres'].min()}'\n
      
      The TV Show with Highest Number of Genres on 'Prime Video' is '{prime_video_genres_most_tvshows['Title'][0]}' : '{prime_video_genres_most_tvshows['Number of Genres'].max()}'\n
      The TV Show with Lowest Number of Genres on 'Prime Video' is '{prime_video_genres_least_tvshows['Title'][0]}' : '{prime_video_genres_least_tvshows['Number of Genres'].min()}'\n
      
      The TV Show with Highest Number of Genres on 'Disney+' is '{disney_genres_most_tvshows['Title'][0]}' : '{disney_genres_most_tvshows['Number of Genres'].max()}'\n
      The TV Show with Lowest Number of Genres on 'Disney+' is '{disney_genres_least_tvshows['Title'][0]}' : '{disney_genres_least_tvshows['Number of Genres'].min()}'\n 
      ''')

      The TV Show with Highest Number of Genres Ever Got is 'Steven Universe' : '11'

      The TV Show with Lowest Number of Genres Ever Got is 'Love & Vets' : '1'

      
      The TV Show with Highest Number of Genres on 'Netflix' is 'Spy Kids: Mission Critical' : '9'

      The TV Show with Lowest Number of Genres on 'Netflix' is 'Louis Theroux: Miami Mega-Jail' : '1'

      
      The TV Show with Highest Number of Genres on 'Hulu' is 'Steven Universe' : '11'

      The TV Show with Lowest Number of Genres on 'Hulu' is 'Tengo Talento, Mucho Talento' : '1'

      
      The TV Show with Highest Number of Genres on 'Prime Video' is 'Infinity Train' : '10'

      The TV Show with Lowest Number of Genres on 'Prime Video' is 'Iconic Characters' : '1'

      
      The TV Show with Highest Number of Genres on 'Disney+' is 'Gargoyles' : '10'

      The TV Show with Lowest Number of Genres on 'Disney+' is 'Love & Vets' : '1'
 
      

print(f'''
      Accross All Platforms the Average Number of Genres is '{round(df_tvshows_count_genres['Number of Genres'].mean(), ndigits = 2)}'\n
      The Average Number of Genres on 'Netflix' is '{round(netflix_genres_tvshows['Number of Genres'].mean(), ndigits = 2)}'\n
      The Average Number of Genres on 'Hulu' is '{round(hulu_genres_tvshows['Number of Genres'].mean(), ndigits = 2)}'\n
      The Average Number of Genres on 'Prime Video' is '{round(prime_video_genres_tvshows['Number of Genres'].mean(), ndigits = 2)}'\n
      The Average Number of Genres on 'Disney+' is '{round(disney_genres_tvshows['Number of Genres'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average Number of Genres is '2.52'

      The Average Number of Genres on 'Netflix' is '2.6'

      The Average Number of Genres on 'Hulu' is '2.74'

      The Average Number of Genres on 'Prime Video' is '2.29'

      The Average Number of Genres on 'Disney+' is '3.74'
 
      

print(f'''
      Accross All Platforms Total Count of Genre is '{df_tvshows_count_genres['Number of Genres'].max()}'\n
      Total Count of Genre on 'Netflix' is '{netflix_genres_tvshows['Number of Genres'].max()}'\n
      Total Count of Genre on 'Hulu' is '{hulu_genres_tvshows['Number of Genres'].max()}'\n
      Total Count of Genre on 'Prime Video' is '{prime_video_genres_tvshows['Number of Genres'].max()}'\n
      Total Count of Genre on 'Disney+' is '{disney_genres_tvshows['Number of Genres'].max()}'\n 
      ''')

      Accross All Platforms Total Count of Genre is '11'

      Total Count of Genre on 'Netflix' is '9'

      Total Count of Genre on 'Hulu' is '11'

      Total Count of Genre on 'Prime Video' is '10'

      Total Count of Genre on 'Disney+' is '10'
 
      

f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(df_tvshows_count_genres['Number of Genres'],bins = 20, kde = True, ax = ax[0])
sns.boxplot(df_tvshows_count_genres['Number of Genres'], ax = ax[1])
plt.show()

# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Number of Genres s Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.histplot(prime_video_genres_tvshows['Number of Genres'], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_genres_tvshows['Number of Genres'], color = 'red', legend = True, kde = True)
sns.histplot(hulu_genres_tvshows['Number of Genres'], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_genres_tvshows['Number of Genres'], color = 'darkblue', legend = True, kde = True) 
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()

df_lan = df_tvshows_genre['Genres'].str.split(',').apply(pd.Series).stack()
del df_tvshows_genre['Genres']
df_lan.index = df_lan.index.droplevel(-1)
df_lan.name = 'Genre'
df_tvshows_genre = df_tvshows_genre.join(df_lan)
df_tvshows_genre.drop_duplicates(inplace = True)

df_tvshows_genre.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Country	Language	...	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider	Genre
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	United States	English	...	60	tv series	3	1	0	0	0	1	Netflix	Action
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	United States	English	...	60	tv series	3	1	0	0	0	1	Netflix	Drama
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	United States	English	...	60	tv series	3	1	0	0	0	1	Netflix	Sci-Fi
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	United States	English	...	60	tv series	3	1	0	0	0	1	Netflix	Thriller
1	2	Philadelphia	1993	13	8.8	80	NA	Charlie Day,Glenn Howerton,Rob McElhenney,Kait...	United States	English	...	22	tv series	18	1	0	0	0	1	Netflix	Comedy

5 rows × 21 columns

genre_count = df_tvshows_genre.groupby('Genre')['Title'].count()
genre_tvshows = df_tvshows_genre.groupby('Genre')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
genre_data_tvshows = pd.concat([genre_count, genre_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count'})
genre_data_tvshows = genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_genre_tvshows = netflix_genre_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)

hulu_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_genre_tvshows = hulu_genre_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)

prime_video_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_genre_tvshows = prime_video_genre_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)

disney_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_genre_tvshows = disney_genre_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)

# Genre with TV Shows Counts - All Platforms Combined
genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)[:10]

	Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
7	Drama	1852	754	552	650	41
4	Comedy	1571	569	600	461	90
2	Animation	985	341	372	312	75
6	Documentary	834	306	138	412	29
8	Family	729	238	195	263	110
0	Action	725	277	264	220	51
5	Crime	673	294	194	231	6
1	Adventure	670	246	217	215	78
9	Fantasy	631	241	246	188	42
23	Thriller	578	238	177	198	9

fig = px.bar(x = genre_data_tvshows['Genre'][:50],
             y = genre_data_tvshows['TV Shows Count'][:50], 
             color = genre_data_tvshows['TV Shows Count'][:50],
             color_continuous_scale = 'Teal_r', 
             labels = { 'x' : 'Genre', 'y' : 'TV Shows Count'},
             title  = 'Major Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

df_genre_high_tvshows = genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_genre_high_tvshows = df_genre_high_tvshows.drop(['index'], axis = 1)
# filter = (genre_data_tvshows['TV Shows Count'] == (genre_data_tvshows['TV Shows Count'].max()))
# df_genre_high_tvshows = genre_data_tvshows[filter]
 
# highest_rated_tvshows = genre_data_tvshows.loc[genre_data_tvshows['TV Shows Count'].idxmax()]
 
print('\nGenre with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_genre_high_tvshows.head(5)

Genre with Highest Ever TV Shows Count are : All Platforms Combined

	Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Drama	1852	754	552	650	41
1	Comedy	1571	569	600	461	90
2	Animation	985	341	372	312	75
3	Documentary	834	306	138	412	29
4	Family	729	238	195	263	110

fig = px.bar(y = df_genre_high_tvshows['Genre'][:15],
             x = df_genre_high_tvshows['TV Shows Count'][:15], 
             color = df_genre_high_tvshows['TV Shows Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
             title  = 'Genre with Highest TV Shows : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

df_genre_low_tvshows = genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_genre_low_tvshows = df_genre_low_tvshows.drop(['index'], axis = 1)
# filter = (genre_data_tvshows['TV Shows Count'] == (genre_data_tvshows['TV Shows Count'].min()))
# df_genre_low_tvshows = genre_data_tvshows[filter]

print('\nGenre with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_genre_low_tvshows.head(5)

Genre with Lowest Ever TV Shows Count are : All Platforms Combined

	Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Western	47	9	11	31	1
1	News	53	10	28	18	0
2	Musical	75	31	14	19	18
3	War	94	38	17	42	0
4	Talk-Show	107	28	48	34	2

fig = px.bar(y = df_genre_low_tvshows['Genre'][:15],
             x = df_genre_low_tvshows['TV Shows Count'][:15], 
             color = df_genre_low_tvshows['TV Shows Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
             title  = 'Genre with Lowest TV Shows Count : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      Total '{genre_data_tvshows['Genre'].unique().shape[0]}' unique Genre Count s were Given, They were Like this,\n
      
      {genre_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Genre'].unique()[:5]}\n
 
      The Highest Ever TV Shows Count Ever Any TV Show Got is '{df_genre_high_tvshows['Genre'][0]}' : '{df_genre_high_tvshows['TV Shows Count'].max()}'\n
 
      The Lowest Ever TV Shows Count Ever Any TV Show Got is '{df_genre_low_tvshows['Genre'][0]}' : '{df_genre_low_tvshows['TV Shows Count'].min()}'\n
      ''')

      Total '26' unique Genre Count s were Given, They were Like this,

      
      ['Drama' 'Comedy' 'Animation' 'Documentary' 'Family']

 
      The Highest Ever TV Shows Count Ever Any TV Show Got is 'Drama' : '1852'

 
      The Lowest Ever TV Shows Count Ever Any TV Show Got is 'Western' : '47'

      

fig = px.pie(genre_data_tvshows[:10], names = 'Genre', values = 'TV Shows Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'TV Shows Count based on Genre')
fig.show()

# netflix_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_genre_tvshows = netflix_genre_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_genre_high_tvshows = df_genre_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_genre_high_tvshows = netflix_genre_high_tvshows.drop(['index'], axis = 1)
 
netflix_genre_low_tvshows = df_genre_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_genre_low_tvshows = netflix_genre_low_tvshows.drop(['index'], axis = 1)
 
netflix_genre_high_tvshows.head(5)

	Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Drama	1852	754	552	650	41
1	Comedy	1571	569	600	461	90
2	Animation	985	341	372	312	75
3	Documentary	834	306	138	412	29
4	Crime	673	294	194	231	6

fig = px.bar(x = netflix_genre_high_tvshows['Genre'][:15],
             y = netflix_genre_high_tvshows['Netflix'][:15], 
             color = netflix_genre_high_tvshows['Netflix'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
             title  = 'Genre with Highest TV Shows : Netflix')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

# hulu_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_genre_tvshows = hulu_genre_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_genre_high_tvshows = df_genre_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_genre_high_tvshows = hulu_genre_high_tvshows.drop(['index'], axis = 1)
 
hulu_genre_low_tvshows = df_genre_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_genre_low_tvshows = hulu_genre_low_tvshows.drop(['index'], axis = 1)
 
hulu_genre_high_tvshows.head(5)

	Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Comedy	1571	569	600	461	90
1	Drama	1852	754	552	650	41
2	Animation	985	341	372	312	75
3	Action	725	277	264	220	51
4	Reality-TV	571	167	247	186	22

fig = px.bar(x = hulu_genre_high_tvshows['Genre'][:15],
             y = hulu_genre_high_tvshows['Hulu'][:15], 
             color = hulu_genre_high_tvshows['Hulu'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
             title  = 'Genre with Highest TV Shows : Hulu')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

# prime_video_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_genre_tvshows = prime_video_genre_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_genre_high_tvshows = df_genre_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_genre_high_tvshows = prime_video_genre_high_tvshows.drop(['index'], axis = 1)
 
prime_video_genre_low_tvshows = df_genre_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_genre_low_tvshows = prime_video_genre_low_tvshows.drop(['index'], axis = 1)
 
prime_video_genre_high_tvshows.head(5)

	Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Drama	1852	754	552	650	41
1	Comedy	1571	569	600	461	90
2	Documentary	834	306	138	412	29
3	Animation	985	341	372	312	75
4	Family	729	238	195	263	110

fig = px.bar(x = prime_video_genre_high_tvshows['Genre'][:15],
             y = prime_video_genre_high_tvshows['Prime Video'][:15], 
             color = prime_video_genre_high_tvshows['Prime Video'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
             title  = 'Genre with Highest TV Shows : Prime Video')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

# disney_genre_tvshows = genre_data_tvshows[genre_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_genre_tvshows = disney_genre_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_genre_high_tvshows = df_genre_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_genre_high_tvshows = disney_genre_high_tvshows.drop(['index'], axis = 1)
 
disney_genre_low_tvshows = df_genre_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_genre_low_tvshows = disney_genre_low_tvshows.drop(['index'], axis = 1)
 
disney_genre_high_tvshows.head(5)

	Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Family	729	238	195	263	110
1	Comedy	1571	569	600	461	90
2	Adventure	670	246	217	215	78
3	Animation	985	341	372	312	75
4	Action	725	277	264	220	51

fig = px.bar(x = disney_genre_high_tvshows['Genre'][:15],
             y = disney_genre_high_tvshows['Disney+'][:15], 
             color = disney_genre_high_tvshows['Disney+'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'Genre', 'x' : 'TV Shows Count'},
             title  = 'Genre with Highest TV Shows : Disney+')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(genre_data_tvshows['TV Shows Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(genre_data_tvshows['TV Shows Count'], ax = ax[1])
plt.show()

# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Genre TV Shows Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(disney_genre_tvshows['Disney+'][:50], color = 'darkblue', legend = True, kde = True)  
sns.histplot(prime_video_genre_tvshows['Prime Video'][:50], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_genre_tvshows['Netflix'][:50], color = 'red', legend = True, kde = True)
sns.histplot(hulu_genre_tvshows['Hulu'][:50], color = 'lightgreen', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Disney+', 'Prime Video', 'Netflix', 'Hulu'])
plt.show()

print(f'''
      The Genre with Highest TV Shows Count Ever Got is '{df_genre_high_tvshows['Genre'][0]}' : '{df_genre_high_tvshows['TV Shows Count'].max()}'\n
      The Genre with Lowest TV Shows Count Ever Got is '{df_genre_low_tvshows['Genre'][0]}' : '{df_genre_low_tvshows['TV Shows Count'].min()}'\n
      
      The Genre with Highest TV Shows Count on 'Netflix' is '{netflix_genre_high_tvshows['Genre'][0]}' : '{netflix_genre_high_tvshows['Netflix'].max()}'\n
      The Genre with Lowest TV Shows Count on 'Netflix' is '{netflix_genre_low_tvshows['Genre'][0]}' : '{netflix_genre_low_tvshows['Netflix'].min()}'\n
      
      The Genre with Highest TV Shows Count on 'Hulu' is '{hulu_genre_high_tvshows['Genre'][0]}' : '{hulu_genre_high_tvshows['Hulu'].max()}'\n
      The Genre with Lowest TV Shows Count on 'Hulu' is '{hulu_genre_low_tvshows['Genre'][0]}' : '{hulu_genre_low_tvshows['Hulu'].min()}'\n
      
      The Genre with Highest TV Shows Count on 'Prime Video' is '{prime_video_genre_high_tvshows['Genre'][0]}' : '{prime_video_genre_high_tvshows['Prime Video'].max()}'\n
      The Genre with Lowest TV Shows Count on 'Prime Video' is '{prime_video_genre_low_tvshows['Genre'][0]}' : '{prime_video_genre_low_tvshows['Prime Video'].min()}'\n
      
      The Genre with Highest TV Shows Count on 'Disney+' is '{disney_genre_high_tvshows['Genre'][0]}' : '{disney_genre_high_tvshows['Disney+'].max()}'\n
      The Genre with Lowest TV Shows Count on 'Disney+' is '{disney_genre_low_tvshows['Genre'][0]}' : '{disney_genre_low_tvshows['Disney+'].min()}'\n 
      ''')

      The Genre with Highest TV Shows Count Ever Got is 'Drama' : '1852'

      The Genre with Lowest TV Shows Count Ever Got is 'Western' : '47'

      
      The Genre with Highest TV Shows Count on 'Netflix' is 'Drama' : '754'

      The Genre with Lowest TV Shows Count on 'Netflix' is 'Western' : '9'

      
      The Genre with Highest TV Shows Count on 'Hulu' is 'Comedy' : '600'

      The Genre with Lowest TV Shows Count on 'Hulu' is 'Western' : '11'

      
      The Genre with Highest TV Shows Count on 'Prime Video' is 'Drama' : '650'

      The Genre with Lowest TV Shows Count on 'Prime Video' is 'News' : '18'

      
      The Genre with Highest TV Shows Count on 'Disney+' is 'Family' : '110'

      The Genre with Lowest TV Shows Count on 'Disney+' is 'War' : '0'
 
      

# Distribution of tvshows genre in each platform
plt.figure(figsize = (20, 5))
plt.title('Genre with TV Shows Count for All Platforms')
sns.violinplot(x = genre_data_tvshows['TV Shows Count'][:100], color = 'gold', legend = True, kde = True, shade = False)
plt.show()

# Distribution of Genre TV Shows Count in each platform
f1, ax1 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = netflix_genre_tvshows['Netflix'][:100], color = 'red', ax = ax1[0])
sns.violinplot(x = hulu_genre_tvshows['Hulu'][:100], color = 'lightgreen', ax = ax1[1])
 
f2, ax2 = plt.subplots(1, 2 , figsize = (20, 5))
sns.violinplot(x = prime_video_genre_tvshows['Prime Video'][:100], color = 'lightblue', ax = ax2[0])
sns.violinplot(x = disney_genre_tvshows['Disney+'][:100], color = 'darkblue', ax = ax2[1])
plt.show()

print(f'''
      Accross All Platforms the Average TV Shows Count of Genre is '{round(genre_data_tvshows['TV Shows Count'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Genre on 'Netflix' is '{round(netflix_genre_tvshows['Netflix'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Genre on 'Hulu' is '{round(hulu_genre_tvshows['Hulu'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Genre on 'Prime Video' is '{round(prime_video_genre_tvshows['Prime Video'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Genre on 'Disney+' is '{round(disney_genre_tvshows['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average TV Shows Count of Genre is '495.88'

      The Average TV Shows Count of Genre on 'Netflix' is '182.96'

      The Average TV Shows Count of Genre on 'Hulu' is '161.42'

      The Average TV Shows Count of Genre on 'Prime Video' is '170.92'

      The Average TV Shows Count of Genre on 'Disney+' is '29.3'
 
      

print(f'''
      Accross All Platforms Total Count of Genre is '{genre_data_tvshows['Genre'].unique().shape[0]}'\n
      Total Count of Genre on 'Netflix' is '{netflix_genre_tvshows['Genre'].unique().shape[0]}'\n
      Total Count of Genre on 'Hulu' is '{hulu_genre_tvshows['Genre'].unique().shape[0]}'\n
      Total Count of Genre on 'Prime Video' is '{prime_video_genre_tvshows['Genre'].unique().shape[0]}'\n
      Total Count of Genre on 'Disney+' is '{disney_genre_tvshows['Genre'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Genre is '26'

      Total Count of Genre on 'Netflix' is '26'

      Total Count of Genre on 'Hulu' is '26'

      Total Count of Genre on 'Prime Video' is '26'

      Total Count of Genre on 'Disney+' is '23'
 
      

plt.figure(figsize = (20, 5))
sns.lineplot(x = genre_data_tvshows['Genre'][:10], y = genre_data_tvshows['Netflix'][:10], color = 'red')
sns.lineplot(x = genre_data_tvshows['Genre'][:10], y = genre_data_tvshows['Hulu'][:10], color = 'lightgreen')
sns.lineplot(x = genre_data_tvshows['Genre'][:10], y = genre_data_tvshows['Prime Video'][:10], color = 'lightblue')
sns.lineplot(x = genre_data_tvshows['Genre'][:10], y = genre_data_tvshows['Disney+'][:10], color = 'darkblue')
plt.xlabel('Genre', fontsize = 20)
plt.ylabel('TV Shows Count', fontsize = 20)
plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_g_ax1 = sns.lineplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_g_ax2 = sns.lineplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_g_ax3 = sns.lineplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_g_ax4 = sns.lineplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
 
plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_g_ax1 = sns.barplot(y = netflix_genre_tvshows['Genre'][:10], x = netflix_genre_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_g_ax2 = sns.barplot(y = hulu_genre_tvshows['Genre'][:10], x = hulu_genre_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_g_ax3 = sns.barplot(y = prime_video_genre_tvshows['Genre'][:10], x = prime_video_genre_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_g_ax4 = sns.barplot(y = disney_genre_tvshows['Genre'][:10], x = disney_genre_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
 
plt.show()

# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Genre  TV Shows Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_genre_tvshows['Netflix'][:10], color = 'red', legend = True)
sns.kdeplot(hulu_genre_tvshows['Hulu'][:10], color = 'green', legend = True)
sns.kdeplot(prime_video_genre_tvshows['Prime Video'][:10], color = 'lightblue', legend = True)
sns.kdeplot(disney_genre_tvshows['Disney+'][:10], color = 'darkblue', legend = True)                                      
                                      
# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_g_ax1 = sns.barplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_g_ax2 = sns.barplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_g_ax3 = sns.barplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_g_ax4 = sns.barplot(y = genre_data_tvshows['Genre'][:10], x = genre_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
 
plt.show()

df_tvshows_mixed_genres.drop(df_tvshows_mixed_genres.loc[df_tvshows_mixed_genres['Genres'] == "NA"].index, inplace = True)
# df_tvshows_mixed_genres = df_tvshows_mixed_genres[df_tvshows_mixed_genres.Genre != "NA"]
df_tvshows_mixed_genres.drop(df_tvshows_mixed_genres.loc[df_tvshows_mixed_genres['Number of Genres'] == 1].index, inplace = True)

df_tvshows_mixed_genres.head(5)

	ID	Title	Year	Age	IMDb	Rotten Tomatoes	Directors	Cast	Genres	Country	...	Runtime	Kind	Seasons	Netflix	Hulu	Prime Video	Disney+	Type	Service Provider	Number of Genres
0	1	Snowpiercer	2013	18	6.9	94	NA	Daveed Diggs,Iddo Goldberg,Mickey Sumner,Aliso...	Action,Drama,Sci-Fi,Thriller	United States	...	60	tv series	3	1	0	0	0	1	Netflix	4
2	3	Roma	2018	18	8.7	93	NA	Kevin McKidd,Ray Stevenson,Polly Walker,Kerry ...	Action,Drama,History,Romance,War	United Kingdom,United States	...	52	tv series	2	1	0	0	0	1	Netflix	5
5	6	Suburra	2015	NR	7.9	91	NA	Alessandro Borghi,Giacomo Ferrara,Filippo Nigr...	Action,Crime	Italy	...	50	tv series	3	1	0	0	0	1	Netflix	2
6	7	A Wednesday!	2008	NR	8.1	NA	Neeraj Pandey	NA	Comedy,Family,Fantasy,Horror	United States	...	104	tv series	1	1	0	0	0	1	Netflix	4
7	8	Retribution	2015	NR	7	28	NA	Joanna Vanderham,John Lynch,Joe Dempsie,Julie ...	Drama,Mystery,Thriller	United Kingdom	...	231	tv series	1	1	0	1	0	1	Netflix	3

5 rows × 22 columns

mixed_genres_count = df_tvshows_mixed_genres.groupby('Genres')['Title'].count()
mixed_genres_tvshows = df_tvshows_mixed_genres.groupby('Genres')[['Netflix', 'Hulu', 'Prime Video', 'Disney+']].sum()
mixed_genres_data_tvshows = pd.concat([mixed_genres_count, mixed_genres_tvshows], axis = 1).reset_index().rename(columns = {'Title' : 'TV Shows Count', 'Genres' : 'Mixed Genre'})
mixed_genres_data_tvshows = mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)

mixed_genres_data_tvshows.head(5)

	Mixed Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
629	Comedy,Drama	92	44	28	29	0
745	Crime,Drama,Mystery,Thriller	79	27	27	32	1
912	Drama,Romance	76	38	18	26	0
739	Crime,Drama,Mystery	67	19	21	33	0
796	Documentary,Crime	66	32	24	20	0

# Mixed Genre with TV Shows Counts - All Platforms Combined
mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)[:10]

	Mixed Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
629	Comedy,Drama	92	44	28	29	0
745	Crime,Drama,Mystery,Thriller	79	27	27	32	1
912	Drama,Romance	76	38	18	26	0
739	Crime,Drama,Mystery	67	19	21	33	0
796	Documentary,Crime	66	32	24	20	0
530	Animation,Family	66	19	10	40	1
720	Crime,Drama	60	29	13	22	0
749	Crime,Drama,Thriller	54	27	8	20	0
663	Comedy,Drama,Romance	51	25	17	13	0
815	Documentary,History	48	14	3	28	3

df_mixed_genres_high_tvshows = mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False).reset_index()
df_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.drop(['index'], axis = 1)
# filter = (mixed_genres_data_tvshows['TV Shows Count'] = =  (mixed_genres_data_tvshows['TV Shows Count'].max()))
# df_mixed_genres_high_tvshows = mixed_genres_data_tvshows[filter]
 
# highest_rated_tvshows = mixed_genres_data_tvshows.loc[mixed_genres_data_tvshows['TV Shows Count'].idxmax()]
 
print('\nMixed Genre with Highest Ever TV Shows Count are : All Platforms Combined\n')
df_mixed_genres_high_tvshows.head(5)

Mixed Genre with Highest Ever TV Shows Count are : All Platforms Combined

	Mixed Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Comedy,Drama	92	44	28	29	0
1	Crime,Drama,Mystery,Thriller	79	27	27	32	1
2	Drama,Romance	76	38	18	26	0
3	Crime,Drama,Mystery	67	19	21	33	0
4	Documentary,Crime	66	32	24	20	0

fig = px.bar(y = df_mixed_genres_high_tvshows['Mixed Genre'][:15],
             x = df_mixed_genres_high_tvshows['TV Shows Count'][:15], 
             color = df_mixed_genres_high_tvshows['TV Shows Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Mixed Genre'},
             title  = 'TV Shows with Highest Number of Mixed Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

df_mixed_genres_low_tvshows = mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = True).reset_index()
df_mixed_genres_low_tvshows = df_mixed_genres_low_tvshows.drop(['index'], axis = 1)
# filter = (mixed_genres_data_tvshows['TV Shows Count'] = =  (mixed_genres_data_tvshows['TV Shows Count'].min()))
# df_mixed_genres_low_tvshows = mixed_genres_data_tvshows[filter]
 
print('\nMixed Genre with Lowest Ever TV Shows Count are : All Platforms Combined\n')
df_mixed_genres_low_tvshows.head(5)

Mixed Genre with Lowest Ever TV Shows Count are : All Platforms Combined

	Mixed Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Short,Comedy,Drama,Fantasy,Romance	1	0	0	1	0
1	Adventure,Biography,Drama,History,Romance	1	0	0	1	0
2	Adventure,Comedy,Crime,Drama,Romance,Thriller	1	1	0	0	0
3	Adventure,Comedy,Drama,Family	1	0	0	1	0
4	Adventure,Comedy,Drama,Family,Fantasy,Horror,M...	1	0	0	1	0

fig = px.bar(y = df_mixed_genres_low_tvshows['Mixed Genre'][:15],
             x = df_mixed_genres_low_tvshows['TV Shows Count'][:15], 
             color = df_mixed_genres_low_tvshows['TV Shows Count'][:15],
             color_continuous_scale = 'Teal_r', 
             labels = { 'y' : 'TV Shows', 'x' : 'Number of Mixed Genre'},
             title  = 'TV Shows with Lowest Number of Mixed Genres : All Platforms')

fig.update_layout(plot_bgcolor = 'white')
fig.show()

print(f'''
      Total '{df_tvshows_genres['Genres'].count()}' Titles are available on All Platforms, out of which\n
      You Can Choose to see TV Shows from Total '{mixed_genres_data_tvshows['Mixed Genre'].unique().shape[0]}' Mixed Genre, They were Like this, \n
 
      {mixed_genres_data_tvshows.sort_values(by = 'TV Shows Count', ascending = False)['Mixed Genre'].head(5).unique()} etc. \n
 
      The Mixed Genre with Highest TV Shows Count have '{mixed_genres_data_tvshows['TV Shows Count'].max()}' TV Shows Available is '{df_mixed_genres_high_tvshows['Mixed Genre'][0]}', &\n
      The Mixed Genre with Lowest TV Shows Count have '{mixed_genres_data_tvshows['TV Shows Count'].min()}' TV Shows Available is '{df_mixed_genres_low_tvshows['Mixed Genre'][0]}'
      ''')

      Total '5109' Titles are available on All Platforms, out of which

      You Can Choose to see TV Shows from Total '1006' Mixed Genre, They were Like this, 

 
      ['Comedy,Drama' 'Crime,Drama,Mystery,Thriller' 'Drama,Romance'
 'Crime,Drama,Mystery' 'Documentary,Crime'] etc. 

 
      The Mixed Genre with Highest TV Shows Count have '92' TV Shows Available is 'Comedy,Drama', &

      The Mixed Genre with Lowest TV Shows Count have '1' TV Shows Available is 'Short,Comedy,Drama,Fantasy,Romance'
      

fig = px.pie(mixed_genres_data_tvshows[:10], names = 'Mixed Genre', values = 'TV Shows Count', color_discrete_sequence = px.colors.sequential.Teal)
fig.update_traces(textposition = 'inside', textinfo = 'percent+label', title = 'TV Shows Count based on Mixed Genre')
fig.show()

# netflix_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
# netflix_mixed_genres_tvshows = netflix_mixed_genres_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
netflix_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_genres_high_tvshows = netflix_mixed_genres_high_tvshows.drop(['index'], axis = 1)
 
netflix_mixed_genres_low_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Netflix', ascending = True).reset_index()
netflix_mixed_genres_low_tvshows = netflix_mixed_genres_low_tvshows.drop(['index'], axis = 1)
 
netflix_mixed_genres_high_tvshows.head(5)

	Mixed Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Comedy,Drama	92	44	28	29	0
1	Drama,Romance	76	38	18	26	0
2	Documentary,Crime	66	32	24	20	0
3	Crime,Drama	60	29	13	22	0
4	Crime,Drama,Mystery,Thriller	79	27	27	32	1

# hulu_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
# hulu_mixed_genres_tvshows = hulu_mixed_genres_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)
 
hulu_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_genres_high_tvshows = hulu_mixed_genres_high_tvshows.drop(['index'], axis = 1)
 
hulu_mixed_genres_low_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Hulu', ascending = True).reset_index()
hulu_mixed_genres_low_tvshows = hulu_mixed_genres_low_tvshows.drop(['index'], axis = 1)
 
hulu_mixed_genres_high_tvshows.head(5)

	Mixed Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Comedy,Drama	92	44	28	29	0
1	Crime,Drama,Mystery,Thriller	79	27	27	32	1
2	Documentary,Crime	66	32	24	20	0
3	Game-Show,Reality-TV	30	6	23	7	0
4	Animation,Comedy	40	7	23	11	2

# prime_video_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
# prime_video_mixed_genres_tvshows = prime_video_mixed_genres_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)
 
prime_video_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_genres_high_tvshows = prime_video_mixed_genres_high_tvshows.drop(['index'], axis = 1)
 
prime_video_mixed_genres_low_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Prime Video', ascending = True).reset_index()
prime_video_mixed_genres_low_tvshows = prime_video_mixed_genres_low_tvshows.drop(['index'], axis = 1)
 
prime_video_mixed_genres_high_tvshows.head(5)

	Mixed Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Animation,Family	66	19	10	40	1
1	Crime,Drama,Mystery	67	19	21	33	0
2	Crime,Drama,Mystery,Thriller	79	27	27	32	1
3	Comedy,Drama	92	44	28	29	0
4	Documentary,History	48	14	3	28	3

# disney_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
# disney_mixed_genres_tvshows = disney_mixed_genres_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)
 
disney_mixed_genres_high_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_genres_high_tvshows = disney_mixed_genres_high_tvshows.drop(['index'], axis = 1)
 
disney_mixed_genres_low_tvshows = df_mixed_genres_high_tvshows.sort_values(by = 'Disney+', ascending = True).reset_index()
disney_mixed_genres_low_tvshows = disney_mixed_genres_low_tvshows.drop(['index'], axis = 1)
 
disney_mixed_genres_high_tvshows.head(5)

	Mixed Genre	TV Shows Count	Netflix	Hulu	Prime Video	Disney+
0	Comedy,Family	48	12	16	18	9
1	Adventure,Drama,Family	9	1	0	1	7
2	Comedy,Drama,Family	13	2	6	0	6
3	Animation,Adventure,Comedy,Family	13	5	2	3	4
4	Animation,Action,Adventure,Comedy,Sci-Fi	6	1	1	0	4

f, ax = plt.subplots(1, 2 , figsize = (20, 5))
sns.distplot(mixed_genres_data_tvshows['TV Shows Count'], bins = 20, kde = True, ax = ax[0])
sns.boxplot(mixed_genres_data_tvshows['TV Shows Count'], ax = ax[1])
plt.show()

# Creating distinct dataframes only with the tvshows present on individual streaming platforms
netflix_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Netflix'] !=  0].sort_values(by = 'Netflix', ascending = False).reset_index()
netflix_mixed_genres_tvshows = netflix_mixed_genres_tvshows.drop(['index', 'Hulu', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)

hulu_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Hulu'] !=  0].sort_values(by = 'Hulu', ascending = False).reset_index()
hulu_mixed_genres_tvshows = hulu_mixed_genres_tvshows.drop(['index', 'Netflix', 'Prime Video', 'Disney+', 'TV Shows Count'], axis = 1)

prime_video_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Prime Video'] !=  0].sort_values(by = 'Prime Video', ascending = False).reset_index()
prime_video_mixed_genres_tvshows = prime_video_mixed_genres_tvshows.drop(['index', 'Netflix', 'Hulu', 'Disney+', 'TV Shows Count'], axis = 1)

disney_mixed_genres_tvshows = mixed_genres_data_tvshows[mixed_genres_data_tvshows['Disney+'] !=  0].sort_values(by = 'Disney+', ascending = False).reset_index()
disney_mixed_genres_tvshows = disney_mixed_genres_tvshows.drop(['index', 'Netflix', 'Hulu', 'Prime Video', 'TV Shows Count'], axis = 1)

# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Genre TV Shows Count Per Platform')
 
# Plotting the information from each dataset into a histogram
 
sns.histplot(prime_video_mixed_genres_tvshows['Prime Video'][:100], color = 'lightblue', legend = True, kde = True)
sns.histplot(netflix_mixed_genres_tvshows['Netflix'][:100], color = 'red', legend = True, kde = True)
sns.histplot(hulu_mixed_genres_tvshows['Hulu'][:100], color = 'lightgreen', legend = True, kde = True)
sns.histplot(disney_mixed_genres_tvshows['Disney+'][:100], color = 'darkblue', legend = True, kde = True)                                
 
# Setting the legend
plt.legend(['Prime Video', 'Netflix', 'Hulu', 'Disney+'])
plt.show()

print(f'''
      The Mixed Genre with Highest TV Shows Count Ever Got is '{df_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{df_mixed_genres_high_tvshows['TV Shows Count'].max()}'\n
      The Mixed Genre with Lowest TV Shows Count Ever Got is '{df_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{df_mixed_genres_low_tvshows['TV Shows Count'].min()}'\n
      
      The Mixed Genre with Highest TV Shows Count on 'Netflix' is '{netflix_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{netflix_mixed_genres_high_tvshows['Netflix'].max()}'\n
      The Mixed Genre with Lowest TV Shows Count on 'Netflix' is '{netflix_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{netflix_mixed_genres_low_tvshows['Netflix'].min()}'\n
      
      The Mixed Genre with Highest TV Shows Count on 'Hulu' is '{hulu_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{hulu_mixed_genres_high_tvshows['Hulu'].max()}'\n
      The Mixed Genre with Lowest TV Shows Count on 'Hulu' is '{hulu_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{hulu_mixed_genres_low_tvshows['Hulu'].min()}'\n
      
      The Mixed Genre with Highest TV Shows Count on 'Prime Video' is '{prime_video_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{prime_video_mixed_genres_high_tvshows['Prime Video'].max()}'\n
      The Mixed Genre with Lowest TV Shows Count on 'Prime Video' is '{prime_video_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{prime_video_mixed_genres_low_tvshows['Prime Video'].min()}'\n
      
      The Mixed Genre with Highest TV Shows Count on 'Disney+' is '{disney_mixed_genres_high_tvshows['Mixed Genre'][0]}' : '{disney_mixed_genres_high_tvshows['Disney+'].max()}'\n
      The Mixed Genre with Lowest TV Shows Count on 'Disney+' is '{disney_mixed_genres_low_tvshows['Mixed Genre'][0]}' : '{disney_mixed_genres_low_tvshows['Disney+'].min()}'\n 
      ''')

      The Mixed Genre with Highest TV Shows Count Ever Got is 'Comedy,Drama' : '92'

      The Mixed Genre with Lowest TV Shows Count Ever Got is 'Short,Comedy,Drama,Fantasy,Romance' : '1'

      
      The Mixed Genre with Highest TV Shows Count on 'Netflix' is 'Comedy,Drama' : '44'

      The Mixed Genre with Lowest TV Shows Count on 'Netflix' is 'Action,Adventure,Fantasy,Mystery,Romance,Sci-Fi' : '0'

      
      The Mixed Genre with Highest TV Shows Count on 'Hulu' is 'Comedy,Drama' : '28'

      The Mixed Genre with Lowest TV Shows Count on 'Hulu' is 'Sport,Talk-Show' : '0'

      
      The Mixed Genre with Highest TV Shows Count on 'Prime Video' is 'Animation,Family' : '40'

      The Mixed Genre with Lowest TV Shows Count on 'Prime Video' is 'Sport,Talk-Show' : '0'

      
      The Mixed Genre with Highest TV Shows Count on 'Disney+' is 'Comedy,Family' : '9'

      The Mixed Genre with Lowest TV Shows Count on 'Disney+' is 'Comedy,Drama' : '0'
 
      

print(f'''
      Accross All Platforms the Average TV Shows Count of Mixed Genre is '{round(mixed_genres_data_tvshows['TV Shows Count'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Mixed Genre on 'Netflix' is '{round(netflix_mixed_genres_tvshows['Netflix'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Mixed Genre on 'Hulu' is '{round(hulu_mixed_genres_tvshows['Hulu'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Mixed Genre on 'Prime Video' is '{round(prime_video_mixed_genres_tvshows['Prime Video'].mean(), ndigits = 2)}'\n
      The Average TV Shows Count of Mixed Genre on 'Disney+' is '{round(disney_mixed_genres_tvshows['Disney+'].mean(), ndigits = 2)}'\n 
      ''')

      Accross All Platforms the Average TV Shows Count of Mixed Genre is '3.21'

      The Average TV Shows Count of Mixed Genre on 'Netflix' is '2.35'

      The Average TV Shows Count of Mixed Genre on 'Hulu' is '2.19'

      The Average TV Shows Count of Mixed Genre on 'Prime Video' is '2.29'

      The Average TV Shows Count of Mixed Genre on 'Disney+' is '1.45'
 
      

print(f'''
      Accross All Platforms Total Count of Mixed Genre is '{mixed_genres_data_tvshows['Mixed Genre'].unique().shape[0]}'\n
      Total Count of Mixed Genre on 'Netflix' is '{netflix_mixed_genres_tvshows['Mixed Genre'].unique().shape[0]}'\n
      Total Count of Mixed Genre on 'Hulu' is '{hulu_mixed_genres_tvshows['Mixed Genre'].unique().shape[0]}'\n
      Total Count of Mixed Genre on 'Prime Video' is '{prime_video_mixed_genres_tvshows['Mixed Genre'].unique().shape[0]}'\n
      Total Count of Mixed Genre on 'Disney+' is '{disney_mixed_genres_tvshows['Mixed Genre'].unique().shape[0]}'\n 
      ''')

      Accross All Platforms Total Count of Mixed Genre is '1006'

      Total Count of Mixed Genre on 'Netflix' is '513'

      Total Count of Mixed Genre on 'Hulu' is '477'

      Total Count of Mixed Genre on 'Prime Video' is '489'

      Total Count of Mixed Genre on 'Disney+' is '100'
 
      

plt.figure(figsize = (20, 5))
sns.lineplot(x = mixed_genres_data_tvshows['Mixed Genre'][:5], y = mixed_genres_data_tvshows['Netflix'][:5], color = 'red')
sns.lineplot(x = mixed_genres_data_tvshows['Mixed Genre'][:5], y = mixed_genres_data_tvshows['Hulu'][:5], color = 'lightgreen')
sns.lineplot(x = mixed_genres_data_tvshows['Mixed Genre'][:5], y = mixed_genres_data_tvshows['Prime Video'][:5], color = 'lightblue')
sns.lineplot(x = mixed_genres_data_tvshows['Mixed Genre'][:5], y = mixed_genres_data_tvshows['Disney+'][:5], color = 'darkblue')
plt.xlabel('Mixed Genre', fontsize = 15)
plt.ylabel('TV Shows Count', fontsize = 15)
plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_g_ax1 = sns.barplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_g_ax2 = sns.barplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_g_ax3 = sns.barplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_g_ax4 = sns.barplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_g_ax1.title.set_text(labels[0])
h_g_ax2.title.set_text(labels[1])
p_g_ax3.title.set_text(labels[2])
d_g_ax4.title.set_text(labels[3])
 
plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 10))
 
n_mg_ax1 = sns.lineplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Netflix'][:10], color = 'red', ax = axes[0, 0])
h_mg_ax2 = sns.lineplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Hulu'][:10], color = 'lightgreen', ax = axes[0, 1])
p_mg_ax3 = sns.lineplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Prime Video'][:10], color = 'lightblue', ax = axes[1, 0])
d_mg_ax4 = sns.lineplot(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['Disney+'][:10], color = 'darkblue', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_mg_ax1.title.set_text(labels[0])
h_mg_ax2.title.set_text(labels[1])
p_mg_ax3.title.set_text(labels[2])
d_mg_ax4.title.set_text(labels[3])
 
plt.show()

# Defining plot size and title
plt.figure(figsize = (20, 5))
plt.title('Mixed Genre  TV Shows Count Per Platform')
 
# Plotting the information from each dataset into a histogram
sns.kdeplot(netflix_mixed_genres_tvshows['Netflix'][:50], color = 'red', legend = True)
sns.kdeplot(hulu_mixed_genres_tvshows['Hulu'][:50], color = 'green', legend = True)
sns.kdeplot(prime_video_mixed_genres_tvshows['Prime Video'][:50], color = 'lightblue', legend = True)
sns.kdeplot(disney_mixed_genres_tvshows['Disney+'][:50], color = 'darkblue', legend = True)                                      

# Setting the legend
plt.legend(['Netflix', 'Hulu', 'Prime Video', 'Disney+'])
plt.show()

fig, axes = plt.subplots(2, 2, figsize = (20 , 20))
 
n_mg_ax1 = sns.barplot(y = netflix_mixed_genres_tvshows['Mixed Genre'][:10], x = netflix_mixed_genres_tvshows['Netflix'][:10], palette = 'Reds_r', ax = axes[0, 0])
h_mg_ax2 = sns.barplot(y = hulu_mixed_genres_tvshows['Mixed Genre'][:10], x = hulu_mixed_genres_tvshows['Hulu'][:10], palette = 'Greens_r', ax = axes[0, 1])
p_mg_ax3 = sns.barplot(y = prime_video_mixed_genres_tvshows['Mixed Genre'][:10], x = prime_video_mixed_genres_tvshows['Prime Video'][:10], palette = 'Blues_r', ax = axes[1, 0])
d_mg_ax4 = sns.barplot(y = disney_mixed_genres_tvshows['Mixed Genre'][:10], x = disney_mixed_genres_tvshows['Disney+'][:10], palette = 'BuPu_r', ax = axes[1, 1])
 
labels = ['Netflix', 'Hulu', 'Prime Video', 'Disney+']
 
n_mg_ax1.title.set_text(labels[0])
h_mg_ax2.title.set_text(labels[1])
p_mg_ax3.title.set_text(labels[2])
d_mg_ax4.title.set_text(labels[3])
 
plt.show()

fig = go.Figure(go.Funnel(y = mixed_genres_data_tvshows['Mixed Genre'][:10], x = mixed_genres_data_tvshows['TV Shows Count'][:10]))
fig.show()

genres = df_tvshows_genre_all['Genres'].str.get_dummies(',')
df_tvshows_genre_all = pd.concat([df_tvshows_genre_all, genres], axis = 1, sort = False)

data_investigate(df_tvshows_genre_all)

No of Rows :  5109
No of Coloums :  47
**************************************************
Colums Names : 
 Index(['ID', 'Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Directors',
       'Cast', 'Genres', 'Country', 'Language', 'Plotline', 'Runtime', 'Kind',
       'Seasons', 'Netflix', 'Hulu', 'Prime Video', 'Disney+', 'Type',
       'Service Provider', 'Action', 'Adventure', 'Animation', 'Biography',
       'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy',
       'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'News',
       'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show',
       'Thriller', 'War', 'Western'],
      dtype='object')
**************************************************
Datatype of Columns : 
 ID                   int64
Title               object
Year                 int64
Age                 object
IMDb                object
Rotten Tomatoes     object
Directors           object
Cast                object
Genres              object
Country             object
Language            object
Plotline            object
Runtime             object
Kind                object
Seasons             object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Service Provider    object
Action               int64
Adventure            int64
Animation            int64
Biography            int64
Comedy               int64
Crime                int64
Documentary          int64
Drama                int64
Family               int64
Fantasy              int64
Game-Show            int64
History              int64
Horror               int64
Music                int64
Musical              int64
Mystery              int64
News                 int64
Reality-TV           int64
Romance              int64
Sci-Fi               int64
Short                int64
Sport                int64
Talk-Show            int64
Thriller             int64
War                  int64
Western              int64
dtype: object
**************************************************
Missing Values : 
Series([], dtype: int64)
**************************************************
Missing vaules %age wise :

ID                  0.0
Title               0.0
Year                0.0
Age                 0.0
IMDb                0.0
Rotten Tomatoes     0.0
Directors           0.0
Cast                0.0
Genres              0.0
Country             0.0
Language            0.0
Plotline            0.0
Runtime             0.0
Kind                0.0
Seasons             0.0
Netflix             0.0
Hulu                0.0
Prime Video         0.0
Disney+             0.0
Type                0.0
Service Provider    0.0
Action              0.0
Adventure           0.0
Animation           0.0
Biography           0.0
Comedy              0.0
Crime               0.0
Documentary         0.0
Drama               0.0
Family              0.0
Fantasy             0.0
Game-Show           0.0
History             0.0
Horror              0.0
Music               0.0
Musical             0.0
Mystery             0.0
News                0.0
Reality-TV          0.0
Romance             0.0
Sci-Fi              0.0
Short               0.0
Sport               0.0
Talk-Show           0.0
Thriller            0.0
War                 0.0
Western             0.0
dtype: float64
**************************************************
Pictorial Representation : 

#Select the features on the basis of ehich you want to cluster
features = df_tvshows_genre_all[['Action', 'Adventure', 'Animation', 'Biography',
                                 'Comedy', 'Crime', 'Documentary', 'Drama', 'Family', 'Fantasy',
                                 'Game-Show', 'History', 'Horror', 'Music', 'Musical', 'Mystery', 'News',
                                 'Reality-TV', 'Romance', 'Sci-Fi', 'Short', 'Sport', 'Talk-Show',
                                 'Thriller', 'War', 'Western']].astype(int)

#Scaling the data
scaler = StandardScaler()
scaled_data = scaler.fit_transform(features)

#Using TSNE
tsne = TSNE(n_components = 2)
transformed_genre = tsne.fit_transform(scaled_data)

#KMeans - Elbow Method
distortions = []
K = range(1, 100)
for k in K:
    kmean = KMeans(n_clusters = k)
    kmean.fit(scaled_data)
    distortions.append(kmean.inertia_)
fig = px.line(x = K, y = distortions, title = 'The Elbow Method Showing The Optimal K', 
              labels = {'x':'No of Clusters', 'y':'Distortions'})
fig.show()

#Kmeans
cluster = KMeans(n_clusters = 27)
group_pred = cluster.fit_predict(scaled_data)

tsne_df = pd.DataFrame(np.column_stack((transformed_genre, group_pred, df_tvshows_genre_all['Title'], df_tvshows_genre_all['Genres'], df_tvshows_genre_all['Service Provider'])), columns = ['X', 'Y', 'Group', 'Title', 'Genres', 'Service Provider'])

fig = px.scatter(tsne_df, x = 'X', y = 'Y', hover_data = ['Title', 'Genres', 'Service Provider'], color = 'Group', 
                 color_discrete_sequence = px.colors.cyclical.IceFire)
fig.show()